blockchain applications research paper

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Open Access

Peer-reviewed

Research Article

A look into the future of blockchain technology

Roles Conceptualization, Data curation, Investigation, Methodology

Affiliation Groupe ALTEN, France

Contributed equally to this work with: Francesco Fontana, Elisa Ughetto

Roles Methodology, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

Affiliation Politecnico di Torino, Corso Duca degli Abruzzi 24, Turin, Italy

Roles Conceptualization, Investigation, Methodology, Project administration, Supervision, Validation, Writing – original draft, Writing – review & editing

Affiliation Politecnico di Torino & Bureau of Entrepreneurial Finance, Corso Duca degli Abruzzi 24, Turin, Italy

• Daniel Levis, 
• Francesco Fontana, 
• Elisa Ughetto

• Published: November 17, 2021
• https://doi.org/10.1371/journal.pone.0258995
• Reader Comments

In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications might affect firms’ organizations, innovations, and strategies by 2030, and, consequently, which societal areas may be mainly affected. We provide a deep understanding of how the adoption of this technology could lead to changes in Europe over multiple dimensions, ranging from business to culture and society, policy and regulation, economy, and technology. From the projections that reached a significant consensus and were given a high probability of occurrence by the experts, we derive four scenarios built around two main dimensions: the digitization of assets and the change in business models.

Citation: Levis D, Fontana F, Ughetto E (2021) A look into the future of blockchain technology. PLoS ONE 16(11): e0258995. https://doi.org/10.1371/journal.pone.0258995

Editor: Alessandro Margherita, University of Salento, ITALY

Received: June 1, 2021; Accepted: October 9, 2021; Published: November 17, 2021

Copyright: © 2021 Levis et al. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper.

Funding: The authors received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

1 Introduction

Over the last few years, the hype and interest around blockchain technology have consistently increased. Practitioners from many industries and sectors have joined an open, yet mainly unstructured, discussion on the potential disruptive capabilities of this newly born technology [ 1 – 3 ]. In principle, the size of the phenomenon could be huge, with latest estimates predicting blockchain to store, by 2025, the 10 per cent of the world’s GDP (about $88tn in 2019) [ 4 ]. However, the complexity of the technology itself and the difficulties in assessing its impact across the different application fields have prevented the social, industrial and scientific communities to agree upon a shared vision of future blockchain-based scenarios. Very fundamental questions are still to be answered. Which blockchain-enabled applications will see the light in the next few years? Which industrial sectors will be mainly affected? How will companies react to potential industry-disruptors? How will the current societal paradigm shift? Which role will policy makers play in enhancing this new paradigm?

Despite the great and undoubted technological innovation linked to this technology, uncertainties and speculation on the potential scenarios still animate the industrial and scientific dialogue [ 5 ]. In particular, it is not yet clear which applications will see the light, and, eventually, what effects these changes will have at a societal level.

In this paper, we use a Delphi approach to investigate whether, and to what extent, blockchain-based applications will affect firms’ organizations, innovations and strategies by 2030, and, consequently, which societal areas will be mainly affected. With this methodology, we aim at reaching experts’ consensus to gain new insights and assess the likelihood about the future of the technology. This is a relevant issue, as blockchain technology applications cover a wide spectrum of areas. Blockchain can be applied vertically within an industry (e.g. disrupting its supply chain) or horizontally across different industries or within single companies (e.g. modifying the internal structures and the modus operandi of the different company functions). Given the number of potential applications and the complexity of the technology, stakeholders are divided into skeptics, who believe the technology is still too immature to become a paradigm in the near future, and enthusiasts, who instead believe that this radical innovation will disrupt many industries and completely change business models and people’s behaviors, like internet did during the 90s.

The literature on blockchain is also widely fragmented. Different works have investigated possible blockchain applications within specific domains, such as finance [ 6 – 8 ], logistics [ 9 ], healthcare [ 10 , 11 ] and education [ 12 ]. However, a holistic approach on possible blockchain-enabled future scenarios is still missing. To our knowledge, the only contribution in this direction is the one by White [ 13 ], who explores blockchain as a source of disruptive innovation exclusively with regard to the business field. We depart from his work to adopt a much wider perspective in this study. In fact, our aim is to obtain a deep understanding on how the adoption of this technology in Europe will lead to changes over multiple dimensions, ranging from business to culture and society, policy and regulation, economy and technology. Thus, our research aims at exploring if a convergence between the two divergent perspectives on blockchain can be found, bringing together experts currently working on blockchain projects to explore the possible changes that the technology will bring to the society by 2030.

Our study outlines an overall agreement among experts that the blockchain technology will have a deep impact on multiple dimensions. In the near future people will likely start using and exploit the blockchain technology potential, without really knowing how the technology behind works, in the same way as they send emails today, ignoring how the digital architecture that allows to exchange bytes of information works. Policy makers and governments will play a crucial role in this respect, by enabling productivity boosts and competitive gains from the companies operating under their jurisdictions. As such, a tight and cooperative relationship between industrial actors and regulatory bodies will be extremely important and auspicial. To this aim, it will be of key importance for all players to understand the real competitive advantage that blockchain can bring to their own industry and market.

This work aims at contributing to the raising blockchain literature by offering a holistic view on possible blockchain-enabled future scenarios in Europe, and to investigate which of the proposed scenarios is more likely to occur. As widely agreed by the academic literature, technological developments dictate the speed and pace at which societies change [ 14 ]. Under this assumption, technological forecasting appears to be a method of fundamental importance to understand “ex-ante” the potential development of technological changes, and their impact on different societal aspects [ 15 ]. Foreseeing future technological trends could help society in understanding possible future scenarios, thus contributing to a better knowledge of the new paradigms our society is heading towards. The work is structured as follows. Section 2 provides an overview on the main research streams upon which this work is based. Section 3 presents the methodology. Results are described in Section 4 and Section 5 concludes the work.

2 Background literature

2.1 the blockchain technology.

As defined by Crosby et al. [ 3 ] a blockchain can be conceptualized as a shared and decentralized ledger of transactions. This chain grows as new blocks (i.e. read transactions or digital events) are appended to it continuously [ 16 , 17 ]. Each transaction in the ledger must be confirmed by the majority of the participants in the system [ 3 , 18 – 21 ]. This means for the community to verify the truthfulness of the new piece of information and to keep the blockchain copies synchronized between all the nodes (i.e. between all the participants to the network) in such a way that everybody agrees which is the chain of blocks to follow [ 19 ]. Thus, when a client executes a transaction (e.g. when it sends some value to another client), it broadcasts the transaction encrypted with a specific technique to the entire network, so that all users in the system receive a notification of the transaction in a few seconds. At that moment, the transaction is “unconfirmed”, since it has not yet been validated by the community. Once the users verify the transaction with a process called mining, a new block is added to the chain. Usually, the miner (i.e. the user participating to the verification process) receives a reward under the form of virtual coins, called cryptocurrencies. Examples of cryptocurrencies are Bitcoins, Ether, Stellar Lumens and many others. Virtual coins can then be used on the blockchain platform to transfer value between users [ 17 – 19 ].

Thanks to a combination of mathematics and cryptography, the transactions between users (i.e. exchange of data and value), once verified by the network and added to the chain, are “almost” unmodifiable and can be considered true with a reasonable level of confidence [ 17 , 19 , 22 ]. These attributes of the technology make it extremely efficient in transferring value between users, solving the problem of trust and thus potentially eliminating the need of a central authority (e.g. a bank) that authorizes and certifies the transactions [ 7 , 23 , 24 ].

The technology can be easily applied to form legally binding agreements among individuals. The digitalized asset, which is the underlying asset of the contract, is called token. A token can be a digitalized share of a company, as well as a real estate property or a car. Through the setting of smart contracts (i.e. digitalized contracts between two parties), the blockchain technology allows users to freely trade digital tokens, and consequently to trade their underling physical assets without the need of a central authority to certify the transaction (OECD, 2020).

2.2 Blockchain technology applications

The academic literature has investigated a wide range of possible blockchain applications within specific domains, such as finance [ 6 – 8 ], logistics [ 9 ], healthcare [ 10 , 11 ] and education [ 12 ].

As mentioned, one of the undoubted advantages of the blockchain technology is the possibility to overcome the problem of trust while transferring value [ 25 ]. Not surprisingly, the technology seems to find more applications in markets where intermediation is currently high, like the financial sector, and in particular the FinTech sector, that has recently experienced a consistent make-over thanks to the diffusion of digital technologies [ 7 , 26 , 27 ]. The implementation of the blockchain technology in the financial markets could provide investors and entrepreneurs with new tools to successfully exchange value and capitals without relying on central authorities, ideally solving the problem of trust. This is among the reasons why many observers believe that the blockchain would become a potential mainstream financial technology in the future [ 28 ]. Blockchain represents an innovation able to completely remodel our current financial system, breaking the old paradigm requiring trusted centralized parties [ 6 – 8 ]. With new blockchain-based automated forms of peer-to-peer lending, individuals having limited or no access to formal financial services could gain access to basic financial services previously reserved to individuals with certified financial records [ 29 ]. Indeed, blockchain technology can provide value across multiple dimensions, by decreasing information asymmetries and reducing related transactional costs [ 30 ]. Initial coin offerings (ICOs) represent one of the most successful blockchain-based applications for financing which has been currently developed. Virtual currencies like Bitcoins can disruptively change the way in which players active in the business of financing new ventures operate [ 7 , 30 – 33 ]. Through an ICO, a company in need of new capital offers digital stocks (named token) to the public. These digital tokens will then be used by investors to pay the future products developed by the financed company [ 30 , 34 , 35 ]. ICOs represents a disruptive tool: entrepreneurs can now finance their ventures without intermediaries and consequently lower the cost of the capital raised [ 31 , 36 ]. However, some threats coming from the technology adoption can also be identified, as blockchain can also lead to higher risks related to the lower level of control intrinsically connected to the technology, especially in the case of asymmetric information between the parties involved.

Disintermediation plays a key role in the healthcare sector as well, where blockchain has recently found numerous applications. Indeed, many players currently need to exchange a huge amount of information to effectively manage the whole sector: from hospitals, to physicians, to patients. The ability to trustfully exchange data and information becomes of undoubted value in this context [ 10 , 11 ]. It should not be difficult to envision blockchain applications in other fields as well. In every sector in which information, value, or goods are supposed to flow between parties, blockchain can enable a trustful connection between the players, with the need of a central body entrusting the transaction. Within supply chain, it can increase security and traceability of goods [ 9 , 37 ]. Within education, it can help in certifying students’ acquired skills, reducing, for example, degree fraud [ 12 ]. To conclude, a recent work from Lumineau et al. [ 38 ] highlights possible implications of the technology in the way collaborations are ruled and executed, shading light on new organizational paradigms. Indeed, the authors show how the intrinsically diverse nature of the technology could strongly affect organizational outcomes, heavily influencing and modifying (possibly improving) the way in which different entities cooperate and collaborate.

3 Research methodology

3.1 forecasting technique: the delphi method.

In the past decade, an increasing number of forecasting techniques has been employed in the academic literature to predict the potential developments induced by technological changes. In particular, the Delphi method, whose term derives from the Greek oracle Delphos, is a systematic and interactive method of prediction, which is based on a panel of experts and is carried out through a series of iterations, called rounds. Many academic works have adopted this method since its development [ 14 , 39 – 44 ]. As the core of the Delphi approach, experts are required to evaluate projections (representations of possible futures) and assess their societal impact and the likelihood that they will occur within a specific time horizon.

While the majority of forecasting methods does not account for the technological implications on the social, economic and political contexts, the Delphi technique allows subjective consideration of changes in interrelated contexts [ 45 ]. Many different variants of the Delphi methodology have been developed according to the needs and goals of each research. For the purpose of this research, we decided to follow the four-steps procedure suggested by Heiko and Darkow [ 46 ] ( Fig 1 ).

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https://doi.org/10.1371/journal.pone.0258995.g001

The first step of the method requires to develop and envisage projections and possible scenarios that might arise through the adoption of the technology. These projections must be short, unequivocal, and concise [ 14 ]. This phase requires researchers to deeply understand the technology by analyzing the existing literature, attending courses and workshops and conducting a number of face-to-face interviews with experts ( Fig 2 ). Once the insights are gathered, the results are synthetized in future projections that will help develop the survey. The second step consists in presenting the study to the panel of selected experts who will take part in the first round of the survey. The main challenge during this phase is to select an appropriate panel of experts and maintain their commitment and response rate. The third step consists in a statistical and quantitative analysis of the answers received and in the selection of the second-round scenarios that experts will need to evaluate again. Through the analysis of the second round of answers, updated scenarios are developed adding to the projections the qualitative and quantitative insights provided by the research. The ultimate goal of this iterative process is to reach consensus among the experts on the scenarios that are most likely to happen in the future.

https://doi.org/10.1371/journal.pone.0258995.g002

3.2 Formulation of the Delphi projections

The formulation of the projections represents a key aspect of the methodology and requires a particular attention and effort. In this phase, the projections that are later tested by the panel of experts are generated. Vagueness and inaccuracy might generate confusion in experts, leading to less meaningful results. To avoid this situation, we developed the projections by means of triangulation: literature review, interviews with experts and participation to workshops and conferences. The analysis of the literature on blockchain technology (and its benefits) allowed us to understand which industries and businesses will be mainly impacted by the technology.

We chose 2030 as a time horizon for the generation of the scenarios. This is a recommended time span for a Delphi study, since a superior period would have become unmanageable to provide relevant advice for strategic development. As reported in Table 1 , projections span among different areas. To the scope of the work, i.e. to grasp a holistic view of the most likely scenarios, it was necessary to investigate a number of multiple dimensions. Projections are related to socio-cultural, policy and regulations, economic, technological and business aspects. As it can be noticed, projections are all structured in the same way, to facilitate their understanding by experts.

https://doi.org/10.1371/journal.pone.0258995.t001

3.2.1 Interviews with experts.

Twelve blockchain experts were interviewed among academics, startups’ founders and professionals working in consultancy firms, banks and legal institutions. The selection of the experts was made in order to get different points of view and a high level of expertise, as provided by the Delphi method guidelines. We conducted interviews that took between thirty and forty-five minutes on average, according to the interviewee’s availability. Each single interview was tailored for each participant by providing guidelines and reflection tips to encourage discussion. However, a certain degree of freedom was given to the expert to allow his/her spontaneous contribution and to gain some original insights that helped in the final design of the future scenarios. Some common aspects were discussed in all interviews generating redundancy and repetition of already emerged scenarios (e.g. ICOs, business model evolution, security and utility tokens, and legal issues). This is one of the reasons why twelve interviews were considered to be sufficient for the purposes of our research.

3.2.2 Conferences.

One of the authors attended three main events in order to strengthen the knowledge about blockchain and have a broader view of its implications in different fields and industries: one in Milan and two in Paris. Of particular notice, the Community Blockchain Week, a blockchain tech-focused initiative organized voluntarily by actors engaged into the technology and with the will and vision to spread the knowledge among citizens. Thanks to various workshops and speeches during the week, it was possible to dive deeper into many aspects of the technology, as well as to meet some knowledgeable experts of various fields, some of which agreed in participating to the research. The event was extremely useful not only to understand how the technology is evolving, but also to see how the community engages itself to spread the knowledge in order to generate more and more interest around it.

3.2.3 Desk research.

We performed desk research to formulate the initial set of projections. Through the survey of the literature, we gained a comprehensive view of all the potential scenarios of the technology. The analysis of consulting companies’ reports also offered a broader vision of future scenarios, thanks to their strategic rather than technical approach [ 1 , 2 ]. This process led to identify 76 projections that represented the basis for a reflection during the expert face-to-face interviews. After screening the relevant articles and reports, a first filtering of the identified 76 projections was made in order to dismiss redundant or incomplete projections, and to keep only the most complete and varied ones. This process reduced the number of projections to 33 and to 20 after the review of two experts.

3.3 The Delphi projections

The formulation of the projections represents the most sensitive part of the research since it influences the whole study. A detailed analysis was carried out in order to avoid mistakes and confusion. In order to facilitate the respondents filling the questionnaire and to avoid any kind of ambiguity, an introduction explaining the meaning of the terminology used in the questionnaire was presented before starting the survey. The developed scenarios were broken down into six macro categories (the same as proposed by Heiko and Darkow [ 46 ]) to guarantee a more complete and systemic view of how the blockchain ecosystem and community can change and shape the future. The choice of 20 projections to be evaluated by experts is in line with prior studies exploiting the Delphi method [ 46 , 47 ]. Parente and Anderson-Parente [ 47 ] have proposed to limit the number of Delphi questions (e.g. to 25 questions) in order to guarantee a high response rate and properly filled-in questionnaires, including only closed answers. We decided to add the possibility to comment the given answers in order to gather additional qualitative data to improve the quality of the results, in line with the methodology proposed by Heiko and Darkow [ 46 ].

3.4 Selection of the panel of experts

As blockchain experts that took part to the survey, we selected individuals working in companies and institutions on the basis of their experience and knowledge of the field. Following Adler and Ziglio [ 48 ] and Heiko and Darkow [ 46 ] four requirements for “expertise” were considered:

• knowledge and experience on blockchain technology;
• capacity and willingness to participate to the Delphi study;
• sufficient time to participate to the Delphi study;
• effective communication skills.

A minimum panel size of 15–25 participants is often required to lead to consistent results. In our case, a panel of 35 experts was reached for the first round. For the reliability of the study the panelists were selected with different backgrounds and profiles. To be aligned with the European focus of the study, we considered experts working in twelve European countries, being France and Italy the ones with the highest number of respondents. The panel characteristics are reported in Figs 3 , 4 and 5 .

https://doi.org/10.1371/journal.pone.0258995.g003

https://doi.org/10.1371/journal.pone.0258995.g004

https://doi.org/10.1371/journal.pone.0258995.g005

3.5 Execution of the Delphi surveys

In line with the methodology proposed by Heiko and Darkow [ 46 ], two rounds of surveys were executed. We decided to carry no more than two rounds because participating to a Delphi study requires a lot of effort and is a time-consuming task for panelists. By limiting the rounds to two, we reached a sufficient number of respondents that led to have valuable results and consistent conclusions. Moreover, since for each scenario the possibility to include a qualitative argumentation was included, the smaller number of iterations worked as a stimulus for the experts to explain the reasons of their quantitative answers.

The survey was carried out following the standards of the Internet-based Delphi, also called e-Delphi [ 39 , 40 ]. Giving the possibility to respondents to answer digitally allowed experts to be more flexible in responding to the survey, ensuring a greater participation. The way the questionnaire was structured was exactly as the e-Delphi website suggests, but for practical reasons we edited the survey using Google Form. Other standards, such as the real-time Delphi solution proposed by several studies [ 14 , 42 , 43 , 49 ] could have led to a better comparison among experts, but would have likely caused more withdraws to the survey.

3.5.1 First round.

In the first round of the survey, the experts assessed the expected probability and impact of the twenty outlined projections. Some Delphi studies [ 50 , 51 ] include a third factor that helps to assess the desirability of a scenario (i.e. how much an expert is in favour of the realization of a prediction). However, we decided not to include this last aspect to make the questionnaire lighter and faster to be filled in, and to reduce drop-outs ( Table 2 ).

https://doi.org/10.1371/journal.pone.0258995.t002

Impact, evaluated at the industry level, was measured on a five-point Likert scale [ 52 ]. Since there is not a general consensus among experts regarding the number of points the scale should have, and due to the general nature of the scenarios, we preferred to use a five-point Likert scale. The corresponding probabilities are: 0%, 25%, 50%, 75% and 100%. Gathering quantitative data allowed to perform a first set of analyses based on descriptive statistics (e.g. mean, median and interquartile range-IQR). We used qualitative data, instead, to build the final scenarios during the fourth step of the forecasting technique. Even though the literature regarding the Delphi method does not suggest a standardized way to analyze consensus, central tendency measures, such as median and mean values, are useful to grasp a first understanding and are frequently accepted and adopted ( Table 3 ). Scenarios with an IQR equal or lower than 1.5 were considered as having reached an acceptable degree of consensus. It should be noticed that most of the projections that achieved the highest probability, having a median value of 75% achieved also the consensus, i.e. IQR below 1.5. This was the case for projections 3, 4, 8, 9, 10, 13, 15, 19, 20.

https://doi.org/10.1371/journal.pone.0258995.t003

These results show that it was easier for experts to find a consensus over the projections that resulted as very likely to occur. Only projection number 18 achieved a high probability score but could not reach a consensus.

3.5.2 Second round.

During the Delphi’s second round only the projections with an IQR above 1.5 (i.e. which did not reach consensus in the first round) were tested. In order to allow the respondents to easily understand the answers that the panel gave as a whole in round one, for each projection a quantitative report was provided. This report was made of a bar chart with the distribution of the first round’s answers and the correspondent qualitative details, i.e. some of the argumentations provided by some of the panelists. Experts were asked to reconsider the likelihood of occurrence of the projections number 1, 5, 7, 11, 12, 14 and 18. The second round was again structured using Google Form. Following the Delphi’s approach, we did not ask again to estimate the impact for each projection, since this would have presumably been not subject to any change. Moreover, we decided to leave the opportunity to offer again some qualitative comments in support of the answers for a better analysis of the results. The number of experts who successfully completed the second round of the survey dropped to 28, i.e. the 80% of the experts that completed Round 1 and 56% of the selected initial panel. Again, we evaluated the central tendency measures for the projections tested during the second round ( Table 4 ).

https://doi.org/10.1371/journal.pone.0258995.t004

In order to provide a more effective and structured analysis of the results, we first report the final summary table of the Delphi survey and then describe the insights obtained from the analysis. It has to be noticed that Table 5 reports quantitative data only, while during the survey qualitative data were collected as well. In presenting the results of this research, both quantitative and qualitative data are used to provide the best possible picture of what the blockchain-based future will look like. Alongside with standard statistics, we build on qualitative insights obtained during the interviews carried on with experts.

https://doi.org/10.1371/journal.pone.0258995.t005

Firstly, it is interesting to analyze which projections, out of the initial 20, reached a significant consensus (IQR <1.5 after the two rounds of the surveys) and were given a high probability of occurrence by the experts. We can summarize the findings in this domain around three major axes: efficiency, security, and innovation.

By 2030, it will be easier, faster and leaner to exchange value and data among users, institutions and countries. Efficiency will boost and uncover innovation potential within companies and societies if these latter will be able to exploit such a new opportunity. Policies will be a necessary pre-requisite for companies to be able to build a competitive edge globally. From this perspective, the capability of central governments to spur innovation with lean and flexible regulations will be a key driver in explaining the ex-post productivity differential among companies belonging to different countries. From the interview with an investment banker part of the BPCE French group (one of the largest banks in France), it emerged how efficiency is often hampered by the lack of an equally efficient regulation. To provide the reader with an interesting example, in 2018, Natixis, the international corporate and investment banking, asset management, insurance and financial services arm of BPCE, entered the Marco Polo consortium, an initiative born to provide a newly conceived trade and supply chain finance platform, leveraging Application Programming Interfaces (APIs) and blockchain technology. Many other leading banks joined the consortium as well. However, as highlighted by the investment banker, the main limiting factor of the consortium, strongly hampering its efficiency and ability to provide a competitive edge, was the “old-style” bureaucracy linked to it. Although transactions were in principle to be executed smoothly, a bulk of legal paperwork was required to approve them formally. In this case, it appears evident that technology often runs faster than policy, consistently lowering its potential. Interestingly, this view is also shared by regulatory bodies. An experienced lawyer and notary, also member of a panel of experts elected by the Italian government to define the national strategy on blockchain, highlighted that, sometimes, regulators working on blockchain-related policies are trying to adapt existing regulations to the new paradigm. Due to the intrinsically different nature of the technology, this could represent a wrong approach. At the same time, building a new set of policies from scratches could represent a challenging task. From this perspective, projections 4 and 5 confirm this insight: policy and technology should come hand in hand to synergically boost productivity. The three projections reached consensus after the two rounds and were assigned a high probability of occurrence. Overall, it is evident that regulatory aspects linked to the adoption of this new technology shall not be underestimated.

As previously mentioned, security, and specifically cybersecurity, is another dimension around which blockchain could bring consistent advantages, as projections 3, 10, 11 and 15 suggest. On this specific aspect, we interviewed a project leader of the World Economic Forum who previously worked for the United Nations for more than ten years. She dealt specifically with digital regulations, justice, and cybersecurity, and in the last three years before the interview, she specifically worked on blockchain implications and how the technology could be implemented in existing ecosystems. Thanks to her experience in the domain, she clearly explained how the blockchain represents a meaningful technology to avoid cyberattacks to sensitive data and digital files. In her opinion, the avoidance of a single point of failure is the main reason behind a possible blockchain adoption over the next years, since cyberattacks are becoming more frequent and dangerous and related costs for companies are exponentially increasing (e.g. 2020 has been a record year for cyber attacks). Consequently, companies will be increasingly investing in distributed ledgers as a form of contingency budget to lower the cybersecurity risk and its related cost. Given the centrality of data in today’s businesses, serious attacks and loss of data could represent a serious threat to business long-term sustainability.

The third relevant aspect on which blockchain will have a strong impact is, not surprisingly, innovation. Although regulation could represent a non-negligible limiting factor, experts foresee many sectors to be impacted by the technology adoption. For example, the financial sector could be heavily affected by this new paradigm. Particularly, companies’ capital structures and their strategic interlink with business models will drive a differential competitive power. Most likely, enterprises will have to rethink their business models to account for the possibility to digitize/tokenize their assets (Projections 8 and 18). The capability in flexibly adapting their service offerings to the new opportunity and the ability to raise, and re-invest, new capitals will shape the global competition landscape across different industrial sectors and geographies. From one side, blockchain will enable new strategic decisions, from the other side, it will be of fundamental importance to build technological capabilities to enable these decisions. The underlying technology behind transactions, equity offering and equity share transfers will most likely be the blockchain (Projections 13 and 16). Disintermediation and the ability to exchange value, information, and data trustfully without a central authority will enable a new way of funding and cooperation on open-source projects (Projection 19). Most likely, people will refer to blockchain systems as they now refer to browsers such as Chrome, Firefox or Internet Explorer. Many blockchains are already available and are constantly improved and developed, and it is foreseeable that this will remain the case in the future. Users will just need to know the characteristics that a blockchain provides to choose the most suitable one for their business and purposes. Blockchain-based systems will require new skills and knowledge that developers and engineers will need to develop. Big efforts will be needed to make the blockchain more and more user friendly and attractive for those who just want to benefit from the immutability, traceability, and security that it intrinsically brings. At the time of the writing and in line with the Abernathy and Utterback model [ 53 ] many players are currently investing and innovating on blockchain to provide services that will satisfy the new market needs.

The opportunity for people to deal freely will in fact generate opportunities that were unforeseeable before. Self-enforcing smart contracts (Projection 20) will let parties to buy and sell products or to rent them with pay-for-use schemes in an automated way, the digitization of shares and assets will allow companies to raise capital in new ways, without the need to rely on banks, venture capitals or traditional IPOs. Indeed, it is important to understand how the digitization of assets can challenge existing investments and the funding industry represented by traditional private equity firms and banks. Blockchain could allow the creation of platforms for the issuance of traditional financial products on a tokenized nature, making it easier, more transparent and cheaper to manage and access these tools for everyone, including both individual savers and SMEs. Two different types of companies can and will operate in the market: those which have blockchain at their core since their foundation, and those which have (or will have) to embark in a digital transformation process to reconvert themselves into blockchain-based enterprises. In both cases, companies are investing to get a competitive advantage over competitors, betting on the technology that is promising to reduce costs and increase efficiency. Once a dominant design in product and services will be achieved, companies that took a different path will likely exit the market, letting firms following the dominant design to gain market shares.

To conclude and to conceptualize the insights we obtained from both quantitative and qualitative data, we derived four scenarios that we organized in a matrix framework, reported in Table 6 . The framework was built around two main dimensions: on one hand the digitization of assets, and on the other hand the change in business models. The proposed framework leads to the identification of four quadrants: scenarios which envision both the digitization of assets and business model changes and scenarios which do not foresee neither of these two changes. These four main development scenarios were completed and analyzed in the light of the conducted interviews and of the quantitative and qualitative data gathered through the Delphi survey. Each quadrant was given a label: Internal Processes, Flow-less Coopetition, Suppliers Potential and Investment Opportunities. When discussing the quadrants, we try to highlight which of the three improvement areas previously identified (efficiency, security, and innovation) are exploited in the discussed scenario.

https://doi.org/10.1371/journal.pone.0258995.t006

To derive relevant insights from the framework, it is useful to start from the bottom left quadrant, Internal Processes. This name was chosen to highlight the absence of any particular evolution for the company at a strategic level through the blockchain adoption. In this case, it is conceivable to use the technology to incrementally improve firms’ operation performances. Blockchain’s main benefits are to increase traceability of transactions and guarantee their immutability. All these characteristics adopted on today’s processes will result in an automation of routine business functions, such as settlements and reconciliation, customs clearance, heavy payments, invoicing, and documentation, boosting operational efficiency and cost performance. In this scenario, security and efficiency will see a consistent improvement.

The top-left scenario shows instead a different perspective, by considering a broader adoption of blockchain that generates new cooperative business models among different stakeholders, potentially even among competitors. This is why it is called Flow-Less Coopetition. In this case, the benefits of blockchain will help at generating a more democratic ecosystem in terms of information. Those actors that base their business models on information asymmetry, having access to key information before others, will need to revisit their business models if they want to stay competitive. It is of interest to notice how big financial institutions, traditionally competing, are now exploring potential collaboration models in the light of this new technology (e.g. JP Morgan Chase, Morgan Stanley). This quadrant envisages an advance in all three blockchain-enabled dimensions: efficiency, security, and innovation.

The bottom-right scenario, called Suppliers Potential, highlights how, thanks to the digitization that blockchain allows, many actors could jump in the market providing solutions to those companies that would like to benefit from the advantages of digitizing their assets, but are lacking means and competences to internally develop them. Those companies would rather outsource the development of blockchain-based solutions. For this reason, the potential for the creation of a remunerative B2B market exists. Even though there are already protocols that are leaders in the market (Hyperledger Fabric and Ethereum), new solutions with different configurations will likely be needed to support different industries and use case solutions. As for the first scenario, also in this context efficiency and security will be mainly affected.

Finally, the last scenario (Investment Opportunities) focuses on the combination between the complete digitization of the assets of a company and the new business models that this major change could generate. As already mentioned in previous paragraphs, industries are experimenting many ways to facilitate the access to capital. Since the explosion of ICOs in 2017, new and easier ways to access capital have become possible and achievable. However, due to their unregulated nature, ICOs still present numerous potential threats (Projection 14 did not reach consensus). For this reason, other solutions, such as STOs (Security Token Offerings), are on the way of being tested. Bringing a higher degree of freedom to investments will allow companies to receive funds from diverse and non-traditional investors, and it will also boost investments by private individuals into early-stage companies. Efficiency and innovation will be at the core of this last scenario.

5 Conclusions

In this paper, we studied different blockchain-based projections and we assessed their likelihood and impact thanks to the participation of a pool of experts. We built our findings around three dimensions (efficiency, security, and innovation) and we derived four scenarios based on experts’ shared vision. Being the current literature widely fragmented, we believe this research represents a useful starting for conceptualizing blockchain potential and implications. While many research papers focus on blockchain specific applications or general reviews of the state of the art, we try to propose a unifying framework building on different typologies of insights and analyses. We merged quantitative observations derived from standard statistics with qualitative insights obtained directly from experts’ opinions.

Overall, we believe our research can constitute a useful tool for many practitioners involved in the innovation ecosystem and for managers of small, medium and large enterprises to look at future possible scenarios in a more rational and systematic way. From one side, a company’s management can use these forecasts as a starting point for the implementation of new strategies. As previously highlighted, blockchain offers endless possibilities. However, the ability to focus on activities and projects with a positive return on investment will be crucial. Firstly, managers will face the choice between insourcing or outsourcing the technological development of the platform. While the former choice ensures higher flexibility, it also generates high development and maintenance costs. Companies which will identify blockchain as their core service will be entitled to adopt this first strategy, while the majority of the enterprises will probably gain better competitive advantages adopting Blockchain as a Service (BaaS) solution. This latter approach will boost companies’ performances, by enhancing new service offerings as well as a new level of operational efficiency, without carrying the burden and costs of technological complexity.

As mentioned, we believe this research provides useful insights for policy makers as well. The adoption of blockchain represents a tremendous technological change bringing along interesting and tangible opportunities. However, different threats can be foreseen. Central authorities do not only solve the problem of trust in certifying value transactions. They also provide essential supervision on the process itself, for example ensuring that information asymmetry is kept at reasonable levels between parties engaging in any sort of contracts, especially in the financial world. Letting people directly exchange value between themselves or allowing companies to easily raise capitals can boost financial efficiency, but also provides room for frauds and ambiguous behaviours. Today, companies which are interested in raising capitals both through innovative tools such as crowdfunding or through traditional entities such as public financial markets, have the duty to disclose relevant information and usually go through a deep process of due diligence. Regulators should ensure the same level of control on companies that will raise money through Initial Coin Offerings or other sort of blockchain-enabled offerings. We believe that the first step towards a fair regulation of this newly born technology is the understanding of its foreseeable impact on the society in the near future. This work aims to be a precious enabler in this direction. As highlighted in the body of this research, it appears fundamental for policy makers, regulators and government to deeply understand the potential upsides and threats of this new technology, and to correctly navigate the different possible blockchain-enabled scenarios. The successful cooperation between companies’ management and regulators could enable significant productivity shifts in the economic tissue of many countries. Failing in efficiently grasping and enhancing these new paradigms from a regulatory perspective could result into a heavy deficit for the competitive edge and productivity of the industrial sectors of the governments’ respective countries, potentially leading to macroeconomic differentials in productivity.

To conclude, this research could be a useful reference for orienting into this complex and dynamic environment, reducing the perceived uncertainty associated to such a new technology. Thanks to the experts’ advice, it is now possible to have a clearer picture of the evolution of blockchain technologies and of the opportunities and threats that the technology will generate. Certain limitations and characteristics of this study must be considered to correctly and effectively take advantage of its results. The main objective of this work was to examine the most disrupting aspects that are likely to occur in Europe by 2030, with a particular focus on how the technology will facilitate financing, reduce costs, increase transparency and, in general, influence firms’ business models. From this point of view, the objectives and assumptions presented at the beginning of this paper can be considered as fully achieved, but further works exploring other industries and geographies are required to get an organic understanding of the new enhanced paradigms.

Our research only paves the way for a better understanding of what a blockchain-based future will look like, as the differences between industries are too large to be analyzed in a single work. Organizations and businesses in the financial world are consistently changing, but it will be necessary also for companies belonging to different sectors to completely rethink their core activities. From this perspective, we believe further works are needed in these directions. We hope researchers will use and explode our framework to further characterize and meticulously describe the new possible paradigms around the multiple dimensions examined in this work.

Supporting information

https://doi.org/10.1371/journal.pone.0258995.s001

• View Article
• Google Scholar
• 9. Dujak D, Sajter D. Blockchain applications in supply chain. In SMART supply network 2019 (pp. 21–46). Springer, Cham.
• PubMed/NCBI
• 15. Firat AK, Woon WL, Madnick S. Technological forecasting–A review. Composite Information Systems Laboratory (CISL), Massachusetts Institute of Technology. 2008 Sep:20.
• 18. Narayanan A, Bonneau J, Felten E, Miller A, Goldfeder S. Bitcoin and cryptocurrency technologies: a comprehensive introduction. Princeton University Press; 2016 Jul 19.
• 28. Jagtiani J, John K. Fintech: the impact on consumers and regulatory responses.
• 35. Catalini C, Gans JS. Initial coin offerings and the value of crypto tokens. National Bureau of Economic Research; 2018 Mar 16.
• 45. Porter AL, Cunningham SW, Banks J, Roper AT, Mason TW, Rossini FA. Forecasting and management of technology. John Wiley & Sons; 2011 Jun 9.
• 48. Adler M, Ziglio E. Gazing into the oracle: The Delphi method and its application to social policy and public health. Jessica Kingsley Publishers; 1996.
• 50. Linstone HA, Turoff M, editors. The delphi method. Reading, MA: Addison-Wesley; 1975 Dec.

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• Published: 04 July 2019

A systematic review of blockchain

• Min Xu   ORCID: orcid.org/0000-0002-3929-7759 1 ,
• Xingtong Chen 1 &
• Gang Kou 1  

Financial Innovation volume  5 , Article number:  27 ( 2019 ) Cite this article

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Blockchain is considered by many to be a disruptive core technology. Although many researchers have realized the importance of blockchain, the research of blockchain is still in its infancy. Consequently, this study reviews the current academic research on blockchain, especially in the subject area of business and economics. Based on a systematic review of the literature retrieved from the Web of Science service, we explore the top-cited articles, most productive countries, and most common keywords. Additionally, we conduct a clustering analysis and identify the following five research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.” Recommendations on future research directions and practical applications are also provided in this paper.

Introduction

The concepts of bitcoin and blockchain were first proposed in 2008 by someone using the pseudonym Satoshi Nakamoto, who described how cryptology and an open distributed ledger can be combined into a digital currency application (Nakamoto 2008 ). At first, the extremely high volatility of bitcoin and the attitudes of many countries toward its complexity restrained its development somewhat, but the advantages of blockchain—which is bitcoin’s underlying technology—attracted increasing attention. Some of the advantages of blockchain include its distributed ledger, decentralization, information transparency, tamper-proof construction, and openness. The evolution of blockchain has been a progressive process. Blockchain is currently delimited to Blockchain 1.0, 2.0, and 3.0, based on their applications. We provide more details on the three generations of blockchain in the Appendix . The application of blockchain technology has extended from digital currency and into finance, and it has even gradually extended into health care, supply chain management, market monitoring, smart energy, and copyright protection (Engelhardt 2017 ; Hyvarinen et al. 2017 ; Kim and Laskowski 2018 ; O'Dair and Beaven 2017 ; Radanovic and Likic 2018 ; Savelyev 2018 ).

Blockchain technology has been studied by a wide variety of academic disciplines. For example, some researchers have studied the underlying technology of blockchain, such as distributed storage, peer-to-peer networking, cryptography, smart contracts, and consensus algorithms (Christidis and Devetsikiotis 2016 ; Cruz et al. 2018 ; Kraft 2016 ). Meanwhile, legal researchers are interested in the regulations and laws governing blockchain-related technology (Kiviat 2015 ; Paech 2017 ). As the old saying goes: scholars in different disciplines have many different analytical perspectives and “speak many different languages.” This paper focuses on analyzing and combing papers in the field of business and economics. We aim to identify the key nodes (e.g., the most influential articles and journals) in the related research and to find the main research themes of blockchain in our discipline. In addition, we hope to offer some recommendations for future research and provide some suggestions for businesses that wish to apply blockchain in practice.

This study will conduct a systematic and objective review that is based on data statistics and analysis. We first describe the overall number and discipline distribution of blockchain-related papers. A total of 756 journal articles were retrieved. Subsequently, we refined the subject area to business and economics, and were able to add 119 articles to our further analysis. We then explored the influential countries, journals, articles, and most common keywords. On the basis of a scientific literature analysis tool, we were able to identify five research themes on blockchain. We believe that this data-driven literature review will be able to more objectively present the status of this research.

The rest of this paper is organized as follows. In the next section, we provided an overview of the existing articles in all of the disciplines. We holistically describe the number of papers related to blockchain and discipline distribution of the literature. We then conduct a further analysis in the subject field of business and economics, where we analyze the countries, publications, highly cited papers, and so on. We also point out the main research themes of this paper, based on CiteSpace. This is followed by recommendations for promising research directions and practical applications. In the last section, we discuss the conclusions and limitations.

Overview of the current research

In our research, we first conducted a search on Web of Science Core Collection (WOS), including four online databases: Science Citation Index Expanded (SCI-EXPANDED), Social Sciences Citation Index (SSCI), Arts & Humanities Citation Index (A&HCI), and Emerging Sources Citation Index (ESCI). We chose WOS because the papers in these databases can typically reflect scholarly attention towards blockchain. When searching the term “blockchain” as a topic, we found a total of 925 records in these databases. After filtering out the less representative record types, we reduced these papers to 756 articles that were then used for further analysis. We extracted the full bibliographic record of the articles that we identified from WOS, including information on the title, author, keywords, abstract, journal, year, and other publication information. These records were then exported to CiteSpace for subsequent analysis. CiteSpace is a scientific literature analysis tool that enables us to visualize trends and patterns in the scientific literature (Chen 2004 ). In this paper, CiteSpace is used to visually represent complex structures for statistical analysis and to conduct cluster analysis.

Table  1 shows the number of academic papers published per year. We have listed the number of all of the publications in WOS, the number of articles in all of the disciplines, and the number of articles in business and economics subjects. It should be noted that we retrieved the literature on March 25, 2019. Therefore, the number of articles in 2019 is relatively small. The number of papers has continued to grow in recent years, which suggests that there is a growing interest in blockchain. All of the extracted papers in WOS were published after 2015, which is seven years after blockchain and bitcoin was first described by Nakamoto. In these initial seven years, many papers were published online or indexed by other databases. However, we have not discussed these papers here. We only chose WOS, representative high-level literature databases. This is the most common way of doing a literature review (Ipek 2019 ).

In the 756 articles that we managed to retrieve, the three most common keywords besides blockchain are bitcoin, smart contract, and cryptocurrency, with the frequency of 113 times, 72 times, and 61 times, respectively. This shows that the majority of the literature mentions the core technology of blockchain and its most widely known application—bitcoin.

In WOS, each article is assigned to one or more subject categories. Therefore, we use CiteSpace to visualize what research areas are involved in current research on blockchain. Figure  1 shows a network of such subject categories. The most common category is Computer Science, which has the largest circle, followed by Engineering and Telecommunications. Business and Economics is also a common subject area for blockchain. Consequently, in the following session, we will conduct further analysis in this field.

Disciplines in blockchain

Articles in business and economics

Given that the main objective of our research was to understand the research of blockchain in the area of economics and management, we conduct an in-depth analysis on the papers in this field. We refined the research area to Business and Economics, and we finally retrieved 119 articles from WOS. In this session, we analyzed their published journals, research topics, citations, and so on, to depict the research status of blockchain in the field of business and economics more comprehensively.

There are several review papers on blockchain. Each of these paper contains a summary of multiple research topics, instead of a single topic. We do not include these literature reviews in our paper. However, it is undeniable that these articles also play an important role on the study of blockchain. For instance, Wang et al. ( 2019 ) investigate the influence of blockchain on supply chain practices and policies. Zhao et al. ( 2016 ) suggest blockchain will widely adopted in finance and lead to many business innovations and research opportunities.

The United States, the United Kingdom, and Germany are the top three countries by the number of papers published on blockchain; the specific data are shown in Table  2 . The United States released more papers than the other countries and it produced more than one-third of the total articles. As of the time of data collection, China contributed 11 papers, ranking fourth. The 119 papers in total are drawn from 17 countries and regions. In contrast, we searched “big data” and “financial technology” in the same way, and found 286 papers on big data that came from 24 countries, while 779 papers on fintech came from 43 countries. This shows that blockchain is still an emerging research field, and it needs more countries and scholars to join in the research effort.

We counted the journals published in these papers and we found that 44 journals published related papers. Table  3 lists the top 11 journals to have published blockchain research. First is “Strategic Change: Briefings in Entrepreneurial Finance,” followed by “Financial Innovation” and “Asia Pacific Journal of Innovation and Entrepreneurship.” The majority of papers in the journal “Strategic Change” were published in 2017, except for one in 2018 and one in 2019. Papers in the journal “Financial Innovation” were generally published in 2016, with one published in 2017 and one in 2019. All five of the papers in the journal “Asia Pacific Journal of Innovation and Entrepreneurship” were published in 2017.

Cited references

Table  4 presents the top six cited publications, which were cited no less than five times. The list consists of three books and three journal articles. Some of these publications introduce blockchain from a technical perspective and some from an application perspective. Swan’s ( 2015 ) book illustrates the application scenarios of blockchain technology. In this book, the author describes that blockchain is essentially a public ledger with potential as a decentralized digital repository of all assets—not only tangible assets but also intangible assets such as votes, software, health data, and ideas. Tapscott and Tapscott’s ( 2016 ) book explains why blockchain technology will fundamentally change the world. Yermack ( 2017 ) points out that blockchain will have a huge impact and will present many challenges to corporate governance. Böhme et al. ( 2015 ) introduce bitcoin, the first and most famous application of blockchain. Narayanan et al. ( 2016 ) also focus on bitcoin and explain how bitcoin works at a technical level. Lansiti and Lakhani ( 2017 ) argue it will take years to truly transform the blockchain because it is a fundamental rather than destructive technology, which will not drive implementation, and companies will need other incentives to adopt blockchain.

Most influential articles

These 119 papers were cited 314 times in total, and 270 times without self-citations. The number of articles that they cited are 221, of which 197 are non-self-citations. The most influential articles with more than 10 citations are listed in Table  5 . The most popular article in our dataset is Lansiti and Lakhani ( 2017 ), with 49 citations in WOS. This suggests that this article has had a strong influence on the research of blockchain. This paper believes there is still a distance to the real application of the blockchain. The other articles describe how blockchain affects and works in various areas, such as financial services, organizational management, and health care. Since blockchain is an emerging technology, it is particularly necessary to explore how to combine blockchains with various industries and fields.

By comparing the journals in Tables 4 and 5 , we find that some journals appeared in both of the lists, such as Financial Innovation. In other words, papers on blockchain are more welcomed in these journals and the journal’s papers are highly recognized by other scholars. Meanwhile, although journals such as Harvard Business Review have only published a few papers related to blockchain, they are highly cited. Consequently, the journals in both of these lists are of great importance.

Research themes

Addressing research themes is crucial to understanding a research field and exploring future research directions. This paper explored the research topic based on keywords. Keywords are representative and concise descriptions of article content. First, we analyzed the most common keywords used by the papers. We find that the top five most frequently used keywords are “blockchain,” “bitcoin,” “cryptocurrency,” “fintech,” and “smart contract.” Although the potential for blockchain applications goes way beyond digital currencies, bitcoin and other cryptocurrencies—as an important blockchain application scenario in the finance industry—were widely discussed in these articles. Smart contracts allow firms to set up automated transactions in blockchains, thus playing a fundamentally supporting role in blockchain applications. Similar to the literature in all of the subject areas, studies in business and economics also frequently use bitcoin, cryptocurrency, and smart contract as their keywords. The difference is that many researchers have combined blockchain with finance, regarding it as an important financial technology.

After analyzing the frequency of keywords, we conducted a keywords clustering analysis to identify the research themes. As shown in Fig.  2 , five clusters were identified through the log-likelihood ratio (LLR) algorithm in Citespace, they are: cluster #0 “economic benefit,” cluster #1 “blockchain technology,” cluster #2 “initial coin offerings,” cluster #3 “fintech revolution,” and cluster #4 “sharing economy.”

Disciplines and topics

Many researchers have studied the economic benefits of blockchain. They suggest the application of blockchain technology to streamline transactions and settlement processes can effectively reduce the costs associated with manual operations. For instance, in the health care sector, blockchain can play an important role in centralizing research data, avoiding prescription drug fraud, and reducing administrative overheads (Engelhardt 2017 ). In the music industry, blockchain could improve the accuracy and availability of copyright data and significantly improve the transparency of the value chain (O'Dair and Beaven 2017 ). Swan ( 2017 ) expound the economic value of block chain through four typical applications, such as digital asset registries, leapfrog technology, long-tail personalized economic services, and payment channels and peer banking services.

The representative paper for cluster “blockchain technology” was published by Lansiti and Lakhani ( 2017 ), who analyze the inherent features of blockchain and pointed out that we still have a lot to do to apply blockchain extensively. Other researchers have explored the characteristics of blockchain technology from multiple perspectives. For example, Xu ( 2016 ) explores the types of fraud and malicious activities that blockchain technology can prevent and identifies attacks to which blockchain remains vulnerable. Meanwhile, Aune et al. ( 2017 ) propose a cryptographic approach to solve information leakage problems on a blockchain.

Initial coin offering (ICO) is also a research topic of great concern to scholars. Many researchers analyze the determinants of the success of initial coin offerings (Adhami et al. 2018 ; Ante et al. 2018 ). For example, Fisch ( 2019 ) assesses the determinants of the amount raised in ICOs and discusses the role of signaling ventures’ technological capabilities in ICOs. Deng et al. ( 2018 ) argue the outright ban on ICOs might hamper revolutionary technological development and they provided some regulatory reform suggestions on the current ICO ban in China.

Many researchers have explored blockchain’s support for various industries. The fintech revolution brought by the blockchain has received extensive attention (Yang and Li 2018 ). Researchers agree that this nascent technology may transform traditional trading methods and practice in financial industry (Ashta and Biot-Paquerot 2018 ; Chen et al. 2017 ; Kim and Sarin 2018 ). For instance, Gomber et al. ( 2018 ) discuss transformations in four areas of financial services: operations management, payments, lending, and deposit services. Dierksmeier and Seele ( 2018 ) address the impact of blockchain technology on the nature of financial transactions from a business ethics perspective.

Another cluster corresponds to the sharing economy. A handful of researchers have focused on this field and they have discussed the supporting role played by blockchain in the sharing economy. Pazaitis et al. ( 2017 ) describe a conceptual economic model of blockchain-based decentralized cooperation that might better support the dynamics of social sharing. Sun et al. ( 2016 ) discuss the contribution of emerging blockchain technologies to the three major factors of the sharing economy (i.e., human, technology, and organization). They also analyze how blockchain-based sharing services contribute to smart cities.

In this section, we will discuss the following issues: (1) What will be the future research directions for blockchain? (2) How can businesses benefit from blockchain? We hope that our discussions will be able to provide guidance for future academic development and social practice.

What will be the future research directions for blockchain?

In view of the five themes mentioned in this paper, we provide some recommendations for future research in this section.

The economic benefits of blockchain have been extensively studied in previous research. For individual businesses, it is important to understand the effects of blockchain applications on the organizational structure, mode of operation, and management model of the business. For the market as a whole, it is important to determine whether blockchain can resolve the market failures that are brought about by information asymmetry, and whether it can increase market efficiency and social welfare. However, understanding the mechanisms through which blockchain influences corporate and market efficiency will require further academic inquiry.

For researchers of blockchain technology, this paper suggests that we should pay more attention to privacy protection and security issues. Despite the fact that all of the blockchain transactions are anonymous and encrypted, there is still a risk of the data being hacked. In the security sector, there is a view that absolute security can never be guaranteed wherever physical contact exists. Consequently, the question of how to share transaction data while also protecting personal data privacy are particularly vital issues for both academic and social practice.

Initial coin offering and cryptocurrency markets have grown rapidly. They bring many interesting questions, such as how to manage digital currencies. Although the majority of the previous research has focused on the determinants of success of initial coin offerings, we believe that future research will discuss how to regulate cryptocurrency and the ICO market. The success of blockchain technology in digital currency applications prior to 2015 caught the attention of many traditional financial institutions. As blockchain has continued to reinvent itself, in 2019 it is now more than capable of meeting the needs of the finance industry. We believe that blockchain is able to achieve large-scale applications in many areas of finance, such as banking, capital markets, Internet finance, and related fields. The deep integration of blockchain technology and fintech will continue to be a promising research direction.

The sharing economy is often defined as a peer-to-peer based activity of sharing goods and services among individuals. In the future, sharing among enterprises may become an important part of the new sharing economy. Consequently, building the interconnection of blockchains may become a distinct trend. These interconnections will facilitate the linkages between processes of identity authentication, supply chain management, and payments in commercial operations. They will also allow for instantaneous information exchange and data coordination among enterprises and industries.

How can businesses benefit from blockchain?

Businesses can leverage blockchains in a variety of ways to gain an advantage over their competitors. They can streamline their core business, reduce transaction costs, and make intellectual property ownership and payments more transparent and automated (Felin and Lakhani 2018 ). Many researchers have discussed the application of blockchain in business. After analyzing these studies, we believe that enterprises can consider applying blockchain technology in the four aspects that follow.

Accounting settlement and crowdfunding

Bitcoin or another virtual currency supported by blockchain technology can help businesses to solve funding-related problems. For instance, cryptocurrencies support companies who wish to implement non-cash payments and accounting settlement. The automation of electronic transaction management accounting improves the level of control of monetary business execution, both internally and externally (Zadorozhnyi et al. 2018 ). In addition, blockchain technology represents an emerging source of venture capital crowdfunding (O'Dair and Owen 2019 ). Investors or founders of enterprises can obtain alternative entrepreneurial finance through token sales or initial coin offerings. Companies can handle financial-related issues more flexibly by holding, transferring, and issuing digital currencies that are based on blockchain technology.

Data storage and sharing

As the most valuable resource, data plays a vital role in every enterprise. Blockchain provide a reliable storage and efficient use of data (Novikov et al. 2018 ). As a decentralized and secure ledger, blockchain can be used to manage digital asset for many kinds of companies (Dutra et al. 2018 ). Decentralized data storage means you do not give the data to a centralized agency but give it instead to people around the world because no one can tamper with the data on the blockchain. Businesses can use blockchain to store data, improve the transparency and security of the data, and prevent the data from being tampered with. At the same time, blockchain also supports data sharing. For instance, all of the key parties in the accounting profession leverage an accountancy blockchain to aggregate and share instances of practitioner misconduct across the country on a nearly real-time basis (Sheldon 2018 ).

Supply chain management

Blockchain technology has the potential to significantly change supply chain management (SCM) (Treiblmaier 2018 ). Recent adoptions of the Internet of Things and blockchain technologies support better supply-chain provenance (Kim and Laskowski 2018 ). When the product goes from the manufacturer to the customer, important data are recorded in the blockchain. Companies can trace products and raw materials to effectively monitor product quality.

Smart trading

Businesses can build smart contracts on blockchain, which is widely used to implement business collaborations in general and inter-organizational business processes in particular. Enterprises can automate transactions based on smart contracts on block chains without manual confirmation. For instance, businesses can file taxes automatically under smart contracts (Vishnevsky and Chekina 2018 ).

Conclusions

This paper reviews 756 articles related to blockchain on the Web of Science Core Collection. It shows that the most common subject area is Computer Science, followed by Engineering, Telecommunications, and Business and Economics. In the research of Business and Economics, several key nodes are identified in the literature, such as the top-cited articles, most productive countries, and most common keywords. After a cluster analysis of the keywords, we identified the five most popular research themes: “economic benefit,” “blockchain technology,” “initial coin offerings,” “fintech revolution,” and “sharing economy.”

As an important emerging technology, blockchain will play a role in many fields. Therefore, we believe that the issues related to commercial applications of blockchain are critical for both academic and social practice. We propose several promising research directions. The first important research direction is understanding the mechanisms through which blockchain influences corporate and market efficiency. The second potential research direction is privacy protection and security issues. The third relates to how to manage digital currencies and how to regulate the cryptocurrency market. The fourth potential research direction is how to deeply integrate blockchain technology and fintech. The final topic is cross-chain technology—if each industry has its own blockchain system, then researchers and developers must discover new ways to exchange data. This is the key to achieving the Internet of Value. Thus, cross-chain technology will become an increasingly important topic as time goes on.

Businesses can benefit considerably from blockchain technology. Therefore, we suggest that the application of blockchain be taken into consideration when businesses have the following requirements: accounting settlement and crowdfunding, data storage and sharing, supply chain management, and smart trading.

Our study has recognized some limitations. First, this paper only analyzes the literature in Web of Science Core Collection databases (WOS), which may lead to the incompleteness of the relevant literature. Second, we filter our literature base on the subject category in WOS. In this process, we may have omitted some relevant research. Third, our recommendations have subjective limitations. We hope to initiate more research and discussions to address these points in the future.

Availability of data and materials

Data used in this paper were collected from Web of Science Core Collection.

Abbreviations

Initial coin offering

Web of Science Core Collection

Adhami S, Giudici G, Martinazzi S (2018) Why do businesses go crypto? An empirical analysis of initial coin offerings. J Econ Bus 100:64–75

Article   Google Scholar  

Ante L, Sandner P, Fiedler I (2018) Blockchain-based ICOs: pure hype or the Dawn of a new era of startup financing? J Risk Financ Manage 11(4):80

Ashta A, Biot-Paquerot G (2018) FinTech evolution: strategic value management issues in a fast changing industry. Strategic Change-Briefings in Entrepreneurial Finance 27(4):301–311

Google Scholar  

Aune RT, Krellenstein A, O'Hara M, Slama O (2017) Footprints on a Blockchain: trading and information leakage in distributed ledgers. J Trading 12(3):5–13

Böhme R, Christin N, Edelman B, Moore T (2015) Bitcoin: economics, technology, and governance. J Econ Perspect 29(2):213–238

Chen C (2004) Searching for intellectual turning points: progressive knowledge domain visualization. Proc Natl Acad Sci U S A 101(suppl:5303–5310

Chen Z, Li Y, Wu Y, Luo J (2017) The transition from traditional banking to mobile internet finance: an organizational innovation perspective - a comparative study of Citibank and ICBC. Financial Innovation 3(1):12

Christidis K, Devetsikiotis M (2016) Blockchains and smart contracts for the internet of things. Ieee Access 4:2292–2303

Crosby M, Pattanayak P, Verma S (2016) Blockchain technology: beyond bitcoin. Appl Innov 2:6–19

Cruz JP, Kaji Y, Yanai N (2018) RBAC-SC: role-based access control using smart contract. Ieee Access 6:12240–12251

Dai J, Vasarhelyi MA (2017) Toward Blockchain-based accounting and assurance. J Inf Syst 31(3):5–21

Deng H, Huang RH, Wu Q (2018) The regulation of initial coin offerings in China: problems, prognoses and prospects. Eur Bus Organ Law Rev 19(3):465–502

Dierksmeier C, Seele P (2018) Cryptocurrencies and business ethics. J Bus Ethics 152(1):1–14

Dutra A, Tumasjan A, Welpe IM (2018) Blockchain is changing how media and entertainment companies compete. MIT Sloan Manag Rev 60(1):39–3+

Engelhardt MA (2017) Hitching healthcare to the chain: an introduction to Blockchain Technology in the Healthcare Sector. Technol Innov Manage Rev 7(10):22–34

Fanning K, Centers DP (2016) Blockchain and its coming impact on financial services. J Corp Account Finance 27(5):53–57

Felin T, Lakhani K (2018) What problems will you solve with Blockchain? MIT Sloan Manag Rev 60(1):32–3+

Fisch C (2019) Initial coin offerings (ICOs) to finance new ventures. J Bus Ventur 34(1):1–22

Gomber P, Kauffman RJ, Parker C, Weber BW (2018) On the Fintech revolution: interpreting the forces of innovation, disruption, and transformation in financial services. J Manag Inf Syst 35(1):220–265

Green J, Newman P (2017) Citizen utilities: the emerging power paradigm. Energy Policy 105:283–293

Hyvarinen H, Risius M, Friis G (2017) A Blockchain-based approach towards overcoming financial fraud in public sector services. Bus Inf Syst Eng 59(6):441–456

Ipek I (2019) Organizational learning in exporting: a bibliometric analysis and critical review of the empirical research. Int Bus Rev 28(3):544–559

Kim HM, Laskowski M (2018) Toward an ontology-driven blockchain design for supply-chain provenance. Intell Syst Account Finance Manage 25(1):18–27

Kim S, Sarin A (2018) Distributed ledger and Blockchain technology: framework and use cases. J Invest Manage 16(3):90–101

Kiviat TI (2015) Beyond bitcoin: issues in regulating blockchain transactions. Duke Law J 65(3):569–608

Kraft D (2016) Difficulty control for blockchain-based consensus systems. Peer-to-Peer Networking and Applications 9(2):397–413

Lansiti M, Lakhani KR (2017) The truth about Blockchain. Harv Bus Rev 95(1):119–127

Mainelli M, Smith M (2015) Sharing ledgers for sharing economies: an exploration of mutual distributed ledgers (aka blockchain technology). J Financial Perspect 3(3):38-58

Nakamoto S (2008) Bitcoin: a peer-to-peer electronic cash system

Narayanan A, Bonneau J, Felten E, Miller A, Goldfeder S (2016). Bitcoin and cryptocurrency technologies: A Comprehensive Introduction. Princeton University Press: 336

Novikov SP, Mikheenko OV, Kulagina NA, Kazakov OD (2018) Digital registry of professional competences of the population drawing on distributed registries and smart contracts technologies. Biznes Informatika Business Inform 46(4):43–53

O'Dair M, Beaven Z (2017) The networked record industry: how blockchain technology could transform the record industry. Strateg Change Brief Entrep Finance 26(5):471–480

O'Dair M, Owen R (2019) Financing new creative enterprise through blockchain technology: opportunities and policy implications. Strateg Change Brief Entrep Finance 28(1):9–17

Paech P (2017) The governance of Blockchain financial networks. Mod Law Rev 80(6):1073–1110

Pazaitis A, De Filippi P, Kostakis V (2017) Blockchain and value systems in the sharing economy: the illustrative case of Backfeed. Technol Forecast Soc Chang 125:105–115

Pieroni A, Scarpato N, Di Nunzio L, Fallucchi F, Raso M (2018) Smarter city: smart energy grid based on blockchain technology. Int J Adv Sci Eng Inf Technol 8(1):298–306

Radanovic I, Likic R (2018) Opportunities for use of Blockchain Technology in Medicine. Appl Health Econ Health Policy 16(5):583–590

Savelyev A (2018) Copyright in the blockchain era: promises and challenges. Comput Law Secur Rev 34(3):550–561

Sheldon MD (2018) Using Blockchain to aggregate and share misconduct issues across the accounting profession. Curr Issues Audit 12(2):A27–A35

Sun J, Yan J, Zhang KZK (2016) Blockchain-based sharing services: what blockchain technology can contribute to smart cities. Financ Innov 2(1):26

Swan M (2015). Blockchain: Blueprint for a New Economy. O'Reilly Media: 152

Swan M (2017) Anticipating the economic benefits of Blockchain. Technol Innov Manage Rev 7(10):6–13

Tapscott D, Tapscott A (2016) Blockchain revolution: how the technology behind bitcoin is changing money, business, and the world. Portfolio: 368

Tapscott D, Tapscott A (2017) How Blockchain will change organizations. MIT Sloan Manag Rev 58(2):10–13

Treiblmaier H (2018) The impact of the blockchain on the supply chain: a theory-based research framework and a call for action. Supply Chain Manage Int J 23(6):545–559

Vishnevsky VP, Chekina VD (2018) Robot vs. tax inspector or how the fourth industrial revolution will change the tax system: a review of problems and solutions. J Tax Reform 4(1):6–26

Wang YL, Han JH, Beynon-Davies P (2019) Understanding blockchain technology for future supply chains: a systematic literature review and research agenda. Supply Chain Manage Int J 24(1):62–84

Xu JJ (2016) Are blockchains immune to all malicious attacks? Financ Innov 2(1):25

Yang D, Li M (2018) Evolutionary approaches and the construction of technology-driven regulations. Emerg Mark Financ Trade 54(14):3256–3271

Yermack D (2017) Corporate governance and Blockchains. Rev Finance 21(1):7–31

Zadorozhnyi ZMV, Murayskyi VV, Shevchuk OA (2018) Management accounting of electronic transactions with the use of cryptocurrencies. Financ Credit Activ Probl Theory Pract 3(26):169–177

Zhao JL, Fan S, Yan J (2016) Overview of business innovations and research opportunities in blockchain and introduction to the special issue. Financ Innov 2(1):28

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This research is supported by grants from National Natural Science Foundation of China (Nos. 71701168 and 71701034).

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Three generations of blockchain

The scope of blockchain applications has increased from virtual currencies to financial applications to the entire social realm. Based on its applications, blockchain is delimited to Blockchain 1.0, 2.0, and 3.0.

Blockchain 1.0

Blockchain 1.0 was related to virtual currencies, such as bitcoin, which was not only the first and most widely used digital currency but it was also the first application of blockchain technology (Mainelli and Smith 2015 ). Digital currencies can reduce many of the costs associated with traditional physical currencies, such as the costs of circulation. Blockchain 1.0 produced a great many applications, one of which was Bitcoin. Most of these applications were digital currencies and tended to be used commercially for small-value payments, foreign exchange, gambling, and money laundering. At this stage, blockchain technology was generally used as a cryptocurrency and for payment systems that relied on cryptocurrency ecosystems.

Blockchain 2.0

Broadly speaking, Blockchain 2.0 includes Bitcoin 2.0, smart-contracts, smart-property, decentralized applications (Dapps), decentralized autonomous organizations (DAOs), and decentralized autonomous corporations (DACs) (Swan 2015 ). However, most people understand Blockchain 2.0 as applications in other areas of finance, where it is mainly used in securities trading, supply chain finance, banking instruments, payment clearing, anti-counterfeiting, establishing credit systems, and mutual insurance. The financial sector requires high levels of security and data integrity, and thus blockchain applications have some inherent advantages. The greatest contribution of Blockchain 2.0 was the idea of using smart-contracts to disrupt traditional currency and payment systems. Recently, the integration of blockchain and smart contract technology has become a popular research topic in problem resolution. For example, Ethereum, Codius, and Hyperledger have established programmable contract language and executable infrastructure to implement smart contracts.

Blockchain 3.0

In ‘Blockchain: Blueprint for a New Economy’, Blockchain 3.0 is described as the application of blockchain in areas other than currency and finance, such as in government, health, science, culture, and the arts (Swan 2015 ). Blockchain 3.0 aims to popularize the technology, and it focuses on the regulation and governance of its decentralization in society. The scope of this type of blockchain and its potential applications suggests that blockchain technology is a moving target (Crosby et al. 2016 ). Blockchain 3.0 envisions a more advanced form of “smart contracts” to establish a distributed organizational unit that makes and is subject to its own laws and which operates with a high degree of autonomy (Pieroni et al. 2018 ).

The integration of blockchain with tokens is an important combination of Blockchain 3.0. Tokens are proofs of digital rights, and blockchain tokens are widely recognized thanks to Ethereum and its ERC20 standard. Based on this standard, anyone can issue a custom token on Ethereum and this token can represent any right or value. Tokens refer to economic activities generated through the creation of encrypted tokens, which are principally but not exclusively based on the ERC20 standard. Tokens can serve as a form of validation of any right, including personal identity, academic diplomas, currency, receipts, keys, event tickets, rebate points, coupons, stocks, and bonds. Consequently, tokens can validate virtually any right that exists within a society. Blockchain is the back-end technology of the new era, while tokens are its front-end economic face. The combination of the two will bring about major societal transformation. Meanwhile, Blockchain 3.0 and its token economy continue to evolve.

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Acknowledgments

This research has received financial support by the i4OPT project (Ref. PROMETEO/2021/065) granted by the Valencian Regional Government; the CADS4.0-II project (Ref. PDC2022–133957-I00) funded by MCIN/AEI / https://doi.org/10.13039/501100011033 and by European Union Next Generation EU/PRTR; and the Horizon Europe Research & Innovation Programme under grant agreement 101092612 (SUN project).

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Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research

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The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice. In this paper we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, research ethics committee members and other actors to engage with challenges and opportunities specifically related to research ethics. In 2022 the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations, 16 governance presentations, and a series of small group and large group discussions. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. In this paper, we highlight central insights arising from GFBR 2022.

We describe the significance of four thematic insights arising from the forum: (1) Appropriateness of building AI, (2) Transferability of AI systems, (3) Accountability for AI decision-making and outcomes, and (4) Individual consent. We then describe eight recommendations for governance leaders to enhance the ethical governance of AI in global health research, addressing issues such as AI impact assessments, environmental values, and fair partnerships.

Conclusions

The 2022 Global Forum on Bioethics in Research illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

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Introduction

The ethical governance of Artificial Intelligence (AI) in health care and public health continues to be an urgent issue for attention in policy, research, and practice [ 1 , 2 , 3 ]. Beyond the growing number of AI applications being implemented in health care, capabilities of AI models such as Large Language Models (LLMs) expand the potential reach and significance of AI technologies across health-related fields [ 4 , 5 ]. Discussion about effective, ethical governance of AI technologies has spanned a range of governance approaches, including government regulation, organizational decision-making, professional self-regulation, and research ethics review [ 6 , 7 , 8 ]. In this paper, we report on central themes related to challenges and strategies for promoting ethics in research involving AI in global health research, arising from the Global Forum on Bioethics in Research (GFBR), held in Cape Town, South Africa in November 2022. Although applications of AI for research, health care, and public health are diverse and advancing rapidly, the insights generated at the forum remain highly relevant from a global health perspective. After summarizing important context for work in this domain, we highlight categories of ethical issues emphasized at the forum for attention from a research ethics perspective internationally. We then outline strategies proposed for research, innovation, and governance to support more ethical AI for global health.

In this paper, we adopt the definition of AI systems provided by the Organization for Economic Cooperation and Development (OECD) as our starting point. Their definition states that an AI system is “a machine-based system that can, for a given set of human-defined objectives, make predictions, recommendations, or decisions influencing real or virtual environments. AI systems are designed to operate with varying levels of autonomy” [ 9 ]. The conceptualization of an algorithm as helping to constitute an AI system, along with hardware, other elements of software, and a particular context of use, illustrates the wide variety of ways in which AI can be applied. We have found it useful to differentiate applications of AI in research as those classified as “AI systems for discovery” and “AI systems for intervention”. An AI system for discovery is one that is intended to generate new knowledge, for example in drug discovery or public health research in which researchers are seeking potential targets for intervention, innovation, or further research. An AI system for intervention is one that directly contributes to enacting an intervention in a particular context, for example informing decision-making at the point of care or assisting with accuracy in a surgical procedure.

The mandate of the GFBR is to take a broad view of what constitutes research and its regulation in global health, with special attention to bioethics in Low- and Middle- Income Countries. AI as a group of technologies demands such a broad view. AI development for health occurs in a variety of environments, including universities and academic health sciences centers where research ethics review remains an important element of the governance of science and innovation internationally [ 10 , 11 ]. In these settings, research ethics committees (RECs; also known by different names such as Institutional Review Boards or IRBs) make decisions about the ethical appropriateness of projects proposed by researchers and other institutional members, ultimately determining whether a given project is allowed to proceed on ethical grounds [ 12 ].

However, research involving AI for health also takes place in large corporations and smaller scale start-ups, which in some jurisdictions fall outside the scope of research ethics regulation. In the domain of AI, the question of what constitutes research also becomes blurred. For example, is the development of an algorithm itself considered a part of the research process? Or only when that algorithm is tested under the formal constraints of a systematic research methodology? In this paper we take an inclusive view, in which AI development is included in the definition of research activity and within scope for our inquiry, regardless of the setting in which it takes place. This broad perspective characterizes the approach to “research ethics” we take in this paper, extending beyond the work of RECs to include the ethical analysis of the wide range of activities that constitute research as the generation of new knowledge and intervention in the world.

Ethical governance of AI in global health

The ethical governance of AI for global health has been widely discussed in recent years. The World Health Organization (WHO) released its guidelines on ethics and governance of AI for health in 2021, endorsing a set of six ethical principles and exploring the relevance of those principles through a variety of use cases. The WHO guidelines also provided an overview of AI governance, defining governance as covering “a range of steering and rule-making functions of governments and other decision-makers, including international health agencies, for the achievement of national health policy objectives conducive to universal health coverage.” (p. 81) The report usefully provided a series of recommendations related to governance of seven domains pertaining to AI for health: data, benefit sharing, the private sector, the public sector, regulation, policy observatories/model legislation, and global governance. The report acknowledges that much work is yet to be done to advance international cooperation on AI governance, especially related to prioritizing voices from Low- and Middle-Income Countries (LMICs) in global dialogue.

One important point emphasized in the WHO report that reinforces the broader literature on global governance of AI is the distribution of responsibility across a wide range of actors in the AI ecosystem. This is especially important to highlight when focused on research for global health, which is specifically about work that transcends national borders. Alami et al. (2020) discussed the unique risks raised by AI research in global health, ranging from the unavailability of data in many LMICs required to train locally relevant AI models to the capacity of health systems to absorb new AI technologies that demand the use of resources from elsewhere in the system. These observations illustrate the need to identify the unique issues posed by AI research for global health specifically, and the strategies that can be employed by all those implicated in AI governance to promote ethically responsible use of AI in global health research.

RECs and the regulation of research involving AI

RECs represent an important element of the governance of AI for global health research, and thus warrant further commentary as background to our paper. Despite the importance of RECs, foundational questions have been raised about their capabilities to accurately understand and address ethical issues raised by studies involving AI. Rahimzadeh et al. (2023) outlined how RECs in the United States are under-prepared to align with recent federal policy requiring that RECs review data sharing and management plans with attention to the unique ethical issues raised in AI research for health [ 13 ]. Similar research in South Africa identified variability in understanding of existing regulations and ethical issues associated with health-related big data sharing and management among research ethics committee members [ 14 , 15 ]. The effort to address harms accruing to groups or communities as opposed to individuals whose data are included in AI research has also been identified as a unique challenge for RECs [ 16 , 17 ]. Doerr and Meeder (2022) suggested that current regulatory frameworks for research ethics might actually prevent RECs from adequately addressing such issues, as they are deemed out of scope of REC review [ 16 ]. Furthermore, research in the United Kingdom and Canada has suggested that researchers using AI methods for health tend to distinguish between ethical issues and social impact of their research, adopting an overly narrow view of what constitutes ethical issues in their work [ 18 ].

The challenges for RECs in adequately addressing ethical issues in AI research for health care and public health exceed a straightforward survey of ethical considerations. As Ferretti et al. (2021) contend, some capabilities of RECs adequately cover certain issues in AI-based health research, such as the common occurrence of conflicts of interest where researchers who accept funds from commercial technology providers are implicitly incentivized to produce results that align with commercial interests [ 12 ]. However, some features of REC review require reform to adequately meet ethical needs. Ferretti et al. outlined weaknesses of RECs that are longstanding and those that are novel to AI-related projects, proposing a series of directions for development that are regulatory, procedural, and complementary to REC functionality. The work required on a global scale to update the REC function in response to the demands of research involving AI is substantial.

These issues take greater urgency in the context of global health [ 19 ]. Teixeira da Silva (2022) described the global practice of “ethics dumping”, where researchers from high income countries bring ethically contentious practices to RECs in low-income countries as a strategy to gain approval and move projects forward [ 20 ]. Although not yet systematically documented in AI research for health, risk of ethics dumping in AI research is high. Evidence is already emerging of practices of “health data colonialism”, in which AI researchers and developers from large organizations in high-income countries acquire data to build algorithms in LMICs to avoid stricter regulations [ 21 ]. This specific practice is part of a larger collection of practices that characterize health data colonialism, involving the broader exploitation of data and the populations they represent primarily for commercial gain [ 21 , 22 ]. As an additional complication, AI algorithms trained on data from high-income contexts are unlikely to apply in straightforward ways to LMIC settings [ 21 , 23 ]. In the context of global health, there is widespread acknowledgement about the need to not only enhance the knowledge base of REC members about AI-based methods internationally, but to acknowledge the broader shifts required to encourage their capabilities to more fully address these and other ethical issues associated with AI research for health [ 8 ].

Although RECs are an important part of the story of the ethical governance of AI for global health research, they are not the only part. The responsibilities of supra-national entities such as the World Health Organization, national governments, organizational leaders, commercial AI technology providers, health care professionals, and other groups continue to be worked out internationally. In this context of ongoing work, examining issues that demand attention and strategies to address them remains an urgent and valuable task.

The GFBR is an annual meeting organized by the World Health Organization and supported by the Wellcome Trust, the US National Institutes of Health, the UK Medical Research Council (MRC) and the South African MRC. The forum aims to bring together ethicists, researchers, policymakers, REC members and other actors to engage with challenges and opportunities specifically related to research ethics. Each year the GFBR meeting includes a series of case studies and keynotes presented in plenary format to an audience of approximately 100 people who have applied and been competitively selected to attend, along with small-group breakout discussions to advance thinking on related issues. The specific topic of the forum changes each year, with past topics including ethical issues in research with people living with mental health conditions (2021), genome editing (2019), and biobanking/data sharing (2018). The forum is intended to remain grounded in the practical challenges of engaging in research ethics, with special interest in low resource settings from a global health perspective. A post-meeting fellowship scheme is open to all LMIC participants, providing a unique opportunity to apply for funding to further explore and address the ethical challenges that are identified during the meeting.

In 2022, the focus of the GFBR was “Ethics of AI in Global Health Research”. The forum consisted of 6 case study presentations (both short and long form) reporting on specific initiatives related to research ethics and AI for health, and 16 governance presentations (both short and long form) reporting on actual approaches to governing AI in different country settings. A keynote presentation from Professor Effy Vayena addressed the topic of the broader context for AI ethics in a rapidly evolving field. A total of 87 participants attended the forum from 31 countries around the world, representing disciplines of bioethics, AI, health policy, health professional practice, research funding, and bioinformatics. The 2-day forum addressed a wide range of themes. The conference report provides a detailed overview of each of the specific topics addressed while a policy paper outlines the cross-cutting themes (both documents are available at the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ ). As opposed to providing a detailed summary in this paper, we aim to briefly highlight central issues raised, solutions proposed, and the challenges facing the research ethics community in the years to come.

In this way, our primary aim in this paper is to present a synthesis of the challenges and opportunities raised at the GFBR meeting and in the planning process, followed by our reflections as a group of authors on their significance for governance leaders in the coming years. We acknowledge that the views represented at the meeting and in our results are a partial representation of the universe of views on this topic; however, the GFBR leadership invested a great deal of resources in convening a deeply diverse and thoughtful group of researchers and practitioners working on themes of bioethics related to AI for global health including those based in LMICs. We contend that it remains rare to convene such a strong group for an extended time and believe that many of the challenges and opportunities raised demand attention for more ethical futures of AI for health. Nonetheless, our results are primarily descriptive and are thus not explicitly grounded in a normative argument. We make effort in the Discussion section to contextualize our results by describing their significance and connecting them to broader efforts to reform global health research and practice.

Uniquely important ethical issues for AI in global health research

Presentations and group dialogue over the course of the forum raised several issues for consideration, and here we describe four overarching themes for the ethical governance of AI in global health research. Brief descriptions of each issue can be found in Table  1 . Reports referred to throughout the paper are available at the GFBR website provided above.

The first overarching thematic issue relates to the appropriateness of building AI technologies in response to health-related challenges in the first place. Case study presentations referred to initiatives where AI technologies were highly appropriate, such as in ear shape biometric identification to more accurately link electronic health care records to individual patients in Zambia (Alinani Simukanga). Although important ethical issues were raised with respect to privacy, trust, and community engagement in this initiative, the AI-based solution was appropriately matched to the challenge of accurately linking electronic records to specific patient identities. In contrast, forum participants raised questions about the appropriateness of an initiative using AI to improve the quality of handwashing practices in an acute care hospital in India (Niyoshi Shah), which led to gaming the algorithm. Overall, participants acknowledged the dangers of techno-solutionism, in which AI researchers and developers treat AI technologies as the most obvious solutions to problems that in actuality demand much more complex strategies to address [ 24 ]. However, forum participants agreed that RECs in different contexts have differing degrees of power to raise issues of the appropriateness of an AI-based intervention.

The second overarching thematic issue related to whether and how AI-based systems transfer from one national health context to another. One central issue raised by a number of case study presentations related to the challenges of validating an algorithm with data collected in a local environment. For example, one case study presentation described a project that would involve the collection of personally identifiable data for sensitive group identities, such as tribe, clan, or religion, in the jurisdictions involved (South Africa, Nigeria, Tanzania, Uganda and the US; Gakii Masunga). Doing so would enable the team to ensure that those groups were adequately represented in the dataset to ensure the resulting algorithm was not biased against specific community groups when deployed in that context. However, some members of these communities might desire to be represented in the dataset, whereas others might not, illustrating the need to balance autonomy and inclusivity. It was also widely recognized that collecting these data is an immense challenge, particularly when historically oppressive practices have led to a low-trust environment for international organizations and the technologies they produce. It is important to note that in some countries such as South Africa and Rwanda, it is illegal to collect information such as race and tribal identities, re-emphasizing the importance for cultural awareness and avoiding “one size fits all” solutions.

The third overarching thematic issue is related to understanding accountabilities for both the impacts of AI technologies and governance decision-making regarding their use. Where global health research involving AI leads to longer-term harms that might fall outside the usual scope of issues considered by a REC, who is to be held accountable, and how? This question was raised as one that requires much further attention, with law being mixed internationally regarding the mechanisms available to hold researchers, innovators, and their institutions accountable over the longer term. However, it was recognized in breakout group discussion that many jurisdictions are developing strong data protection regimes related specifically to international collaboration for research involving health data. For example, Kenya’s Data Protection Act requires that any internationally funded projects have a local principal investigator who will hold accountability for how data are shared and used [ 25 ]. The issue of research partnerships with commercial entities was raised by many participants in the context of accountability, pointing toward the urgent need for clear principles related to strategies for engagement with commercial technology companies in global health research.

The fourth and final overarching thematic issue raised here is that of consent. The issue of consent was framed by the widely shared recognition that models of individual, explicit consent might not produce a supportive environment for AI innovation that relies on the secondary uses of health-related datasets to build AI algorithms. Given this recognition, approaches such as community oversight of health data uses were suggested as a potential solution. However, the details of implementing such community oversight mechanisms require much further attention, particularly given the unique perspectives on health data in different country settings in global health research. Furthermore, some uses of health data do continue to require consent. One case study of South Africa, Nigeria, Kenya, Ethiopia and Uganda suggested that when health data are shared across borders, individual consent remains necessary when data is transferred from certain countries (Nezerith Cengiz). Broader clarity is necessary to support the ethical governance of health data uses for AI in global health research.

Recommendations for ethical governance of AI in global health research

Dialogue at the forum led to a range of suggestions for promoting ethical conduct of AI research for global health, related to the various roles of actors involved in the governance of AI research broadly defined. The strategies are written for actors we refer to as “governance leaders”, those people distributed throughout the AI for global health research ecosystem who are responsible for ensuring the ethical and socially responsible conduct of global health research involving AI (including researchers themselves). These include RECs, government regulators, health care leaders, health professionals, corporate social accountability officers, and others. Enacting these strategies would bolster the ethical governance of AI for global health more generally, enabling multiple actors to fulfill their roles related to governing research and development activities carried out across multiple organizations, including universities, academic health sciences centers, start-ups, and technology corporations. Specific suggestions are summarized in Table  2 .

First, forum participants suggested that governance leaders including RECs, should remain up to date on recent advances in the regulation of AI for health. Regulation of AI for health advances rapidly and takes on different forms in jurisdictions around the world. RECs play an important role in governance, but only a partial role; it was deemed important for RECs to acknowledge how they fit within a broader governance ecosystem in order to more effectively address the issues within their scope. Not only RECs but organizational leaders responsible for procurement, researchers, and commercial actors should all commit to efforts to remain up to date about the relevant approaches to regulating AI for health care and public health in jurisdictions internationally. In this way, governance can more adequately remain up to date with advances in regulation.

Second, forum participants suggested that governance leaders should focus on ethical governance of health data as a basis for ethical global health AI research. Health data are considered the foundation of AI development, being used to train AI algorithms for various uses [ 26 ]. By focusing on ethical governance of health data generation, sharing, and use, multiple actors will help to build an ethical foundation for AI development among global health researchers.

Third, forum participants believed that governance processes should incorporate AI impact assessments where appropriate. An AI impact assessment is the process of evaluating the potential effects, both positive and negative, of implementing an AI algorithm on individuals, society, and various stakeholders, generally over time frames specified in advance of implementation [ 27 ]. Although not all types of AI research in global health would warrant an AI impact assessment, this is especially relevant for those studies aiming to implement an AI system for intervention into health care or public health. Organizations such as RECs can use AI impact assessments to boost understanding of potential harms at the outset of a research project, encouraging researchers to more deeply consider potential harms in the development of their study.

Fourth, forum participants suggested that governance decisions should incorporate the use of environmental impact assessments, or at least the incorporation of environment values when assessing the potential impact of an AI system. An environmental impact assessment involves evaluating and anticipating the potential environmental effects of a proposed project to inform ethical decision-making that supports sustainability [ 28 ]. Although a relatively new consideration in research ethics conversations [ 29 ], the environmental impact of building technologies is a crucial consideration for the public health commitment to environmental sustainability. Governance leaders can use environmental impact assessments to boost understanding of potential environmental harms linked to AI research projects in global health over both the shorter and longer terms.

Fifth, forum participants suggested that governance leaders should require stronger transparency in the development of AI algorithms in global health research. Transparency was considered essential in the design and development of AI algorithms for global health to ensure ethical and accountable decision-making throughout the process. Furthermore, whether and how researchers have considered the unique contexts into which such algorithms may be deployed can be surfaced through stronger transparency, for example in describing what primary considerations were made at the outset of the project and which stakeholders were consulted along the way. Sharing information about data provenance and methods used in AI development will also enhance the trustworthiness of the AI-based research process.

Sixth, forum participants suggested that governance leaders can encourage or require community engagement at various points throughout an AI project. It was considered that engaging patients and communities is crucial in AI algorithm development to ensure that the technology aligns with community needs and values. However, participants acknowledged that this is not a straightforward process. Effective community engagement requires lengthy commitments to meeting with and hearing from diverse communities in a given setting, and demands a particular set of skills in communication and dialogue that are not possessed by all researchers. Encouraging AI researchers to begin this process early and build long-term partnerships with community members is a promising strategy to deepen community engagement in AI research for global health. One notable recommendation was that research funders have an opportunity to incentivize and enable community engagement with funds dedicated to these activities in AI research in global health.

Seventh, forum participants suggested that governance leaders can encourage researchers to build strong, fair partnerships between institutions and individuals across country settings. In a context of longstanding imbalances in geopolitical and economic power, fair partnerships in global health demand a priori commitments to share benefits related to advances in medical technologies, knowledge, and financial gains. Although enforcement of this point might be beyond the remit of RECs, commentary will encourage researchers to consider stronger, fairer partnerships in global health in the longer term.

Eighth, it became evident that it is necessary to explore new forms of regulatory experimentation given the complexity of regulating a technology of this nature. In addition, the health sector has a series of particularities that make it especially complicated to generate rules that have not been previously tested. Several participants highlighted the desire to promote spaces for experimentation such as regulatory sandboxes or innovation hubs in health. These spaces can have several benefits for addressing issues surrounding the regulation of AI in the health sector, such as: (i) increasing the capacities and knowledge of health authorities about this technology; (ii) identifying the major problems surrounding AI regulation in the health sector; (iii) establishing possibilities for exchange and learning with other authorities; (iv) promoting innovation and entrepreneurship in AI in health; and (vi) identifying the need to regulate AI in this sector and update other existing regulations.

Ninth and finally, forum participants believed that the capabilities of governance leaders need to evolve to better incorporate expertise related to AI in ways that make sense within a given jurisdiction. With respect to RECs, for example, it might not make sense for every REC to recruit a member with expertise in AI methods. Rather, it will make more sense in some jurisdictions to consult with members of the scientific community with expertise in AI when research protocols are submitted that demand such expertise. Furthermore, RECs and other approaches to research governance in jurisdictions around the world will need to evolve in order to adopt the suggestions outlined above, developing processes that apply specifically to the ethical governance of research using AI methods in global health.

Research involving the development and implementation of AI technologies continues to grow in global health, posing important challenges for ethical governance of AI in global health research around the world. In this paper we have summarized insights from the 2022 GFBR, focused specifically on issues in research ethics related to AI for global health research. We summarized four thematic challenges for governance related to AI in global health research and nine suggestions arising from presentations and dialogue at the forum. In this brief discussion section, we present an overarching observation about power imbalances that frames efforts to evolve the role of governance in global health research, and then outline two important opportunity areas as the field develops to meet the challenges of AI in global health research.

Dialogue about power is not unfamiliar in global health, especially given recent contributions exploring what it would mean to de-colonize global health research, funding, and practice [ 30 , 31 ]. Discussions of research ethics applied to AI research in global health contexts are deeply infused with power imbalances. The existing context of global health is one in which high-income countries primarily located in the “Global North” charitably invest in projects taking place primarily in the “Global South” while recouping knowledge, financial, and reputational benefits [ 32 ]. With respect to AI development in particular, recent examples of digital colonialism frame dialogue about global partnerships, raising attention to the role of large commercial entities and global financial capitalism in global health research [ 21 , 22 ]. Furthermore, the power of governance organizations such as RECs to intervene in the process of AI research in global health varies widely around the world, depending on the authorities assigned to them by domestic research governance policies. These observations frame the challenges outlined in our paper, highlighting the difficulties associated with making meaningful change in this field.

Despite these overarching challenges of the global health research context, there are clear strategies for progress in this domain. Firstly, AI innovation is rapidly evolving, which means approaches to the governance of AI for health are rapidly evolving too. Such rapid evolution presents an important opportunity for governance leaders to clarify their vision and influence over AI innovation in global health research, boosting the expertise, structure, and functionality required to meet the demands of research involving AI. Secondly, the research ethics community has strong international ties, linked to a global scholarly community that is committed to sharing insights and best practices around the world. This global community can be leveraged to coordinate efforts to produce advances in the capabilities and authorities of governance leaders to meaningfully govern AI research for global health given the challenges summarized in our paper.

Limitations

Our paper includes two specific limitations that we address explicitly here. First, it is still early in the lifetime of the development of applications of AI for use in global health, and as such, the global community has had limited opportunity to learn from experience. For example, there were many fewer case studies, which detail experiences with the actual implementation of an AI technology, submitted to GFBR 2022 for consideration than was expected. In contrast, there were many more governance reports submitted, which detail the processes and outputs of governance processes that anticipate the development and dissemination of AI technologies. This observation represents both a success and a challenge. It is a success that so many groups are engaging in anticipatory governance of AI technologies, exploring evidence of their likely impacts and governing technologies in novel and well-designed ways. It is a challenge that there is little experience to build upon of the successful implementation of AI technologies in ways that have limited harms while promoting innovation. Further experience with AI technologies in global health will contribute to revising and enhancing the challenges and recommendations we have outlined in our paper.

Second, global trends in the politics and economics of AI technologies are evolving rapidly. Although some nations are advancing detailed policy approaches to regulating AI more generally, including for uses in health care and public health, the impacts of corporate investments in AI and political responses related to governance remain to be seen. The excitement around large language models (LLMs) and large multimodal models (LMMs) has drawn deeper attention to the challenges of regulating AI in any general sense, opening dialogue about health sector-specific regulations. The direction of this global dialogue, strongly linked to high-profile corporate actors and multi-national governance institutions, will strongly influence the development of boundaries around what is possible for the ethical governance of AI for global health. We have written this paper at a point when these developments are proceeding rapidly, and as such, we acknowledge that our recommendations will need updating as the broader field evolves.

Ultimately, coordination and collaboration between many stakeholders in the research ethics ecosystem will be necessary to strengthen the ethical governance of AI in global health research. The 2022 GFBR illustrated several innovations in ethical governance of AI for global health research, as well as several areas in need of urgent attention internationally. This summary is intended to inform international and domestic efforts to strengthen research ethics and support the evolution of governance leadership to meet the demands of AI in global health research.

Data availability

All data and materials analyzed to produce this paper are available on the GFBR website: https://www.gfbr.global/past-meetings/16th-forum-cape-town-south-africa-29-30-november-2022/ .

Clark P, Kim J, Aphinyanaphongs Y, Marketing, Food US. Drug Administration Clearance of Artificial Intelligence and Machine Learning Enabled Software in and as Medical devices: a systematic review. JAMA Netw Open. 2023;6(7):e2321792–2321792.

Article   Google Scholar  

Potnis KC, Ross JS, Aneja S, Gross CP, Richman IB. Artificial intelligence in breast cancer screening: evaluation of FDA device regulation and future recommendations. JAMA Intern Med. 2022;182(12):1306–12.

Siala H, Wang Y. SHIFTing artificial intelligence to be responsible in healthcare: a systematic review. Soc Sci Med. 2022;296:114782.

Yang X, Chen A, PourNejatian N, Shin HC, Smith KE, Parisien C, et al. A large language model for electronic health records. NPJ Digit Med. 2022;5(1):194.

Meskó B, Topol EJ. The imperative for regulatory oversight of large language models (or generative AI) in healthcare. NPJ Digit Med. 2023;6(1):120.

Jobin A, Ienca M, Vayena E. The global landscape of AI ethics guidelines. Nat Mach Intell. 2019;1(9):389–99.

Minssen T, Vayena E, Cohen IG. The challenges for Regulating Medical Use of ChatGPT and other large Language models. JAMA. 2023.

Ho CWL, Malpani R. Scaling up the research ethics framework for healthcare machine learning as global health ethics and governance. Am J Bioeth. 2022;22(5):36–8.

Yeung K. Recommendation of the council on artificial intelligence (OECD). Int Leg Mater. 2020;59(1):27–34.

Maddox TM, Rumsfeld JS, Payne PR. Questions for artificial intelligence in health care. JAMA. 2019;321(1):31–2.

Dzau VJ, Balatbat CA, Ellaissi WF. Revisiting academic health sciences systems a decade later: discovery to health to population to society. Lancet. 2021;398(10318):2300–4.

Ferretti A, Ienca M, Sheehan M, Blasimme A, Dove ES, Farsides B, et al. Ethics review of big data research: what should stay and what should be reformed? BMC Med Ethics. 2021;22(1):1–13.

Rahimzadeh V, Serpico K, Gelinas L. Institutional review boards need new skills to review data sharing and management plans. Nat Med. 2023;1–3.

Kling S, Singh S, Burgess TL, Nair G. The role of an ethics advisory committee in data science research in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–3.

Google Scholar  

Cengiz N, Kabanda SM, Esterhuizen TM, Moodley K. Exploring perspectives of research ethics committee members on the governance of big data in sub-saharan Africa. South Afr J Sci. 2023;119(5–6):1–9.

Doerr M, Meeder S. Big health data research and group harm: the scope of IRB review. Ethics Hum Res. 2022;44(4):34–8.

Ballantyne A, Stewart C. Big data and public-private partnerships in healthcare and research: the application of an ethics framework for big data in health and research. Asian Bioeth Rev. 2019;11(3):315–26.

Samuel G, Chubb J, Derrick G. Boundaries between research ethics and ethical research use in artificial intelligence health research. J Empir Res Hum Res Ethics. 2021;16(3):325–37.

Murphy K, Di Ruggiero E, Upshur R, Willison DJ, Malhotra N, Cai JC, et al. Artificial intelligence for good health: a scoping review of the ethics literature. BMC Med Ethics. 2021;22(1):1–17.

Teixeira da Silva JA. Handling ethics dumping and neo-colonial research: from the laboratory to the academic literature. J Bioethical Inq. 2022;19(3):433–43.

Ferryman K. The dangers of data colonialism in precision public health. Glob Policy. 2021;12:90–2.

Couldry N, Mejias UA. Data colonialism: rethinking big data’s relation to the contemporary subject. Telev New Media. 2019;20(4):336–49.

Organization WH. Ethics and governance of artificial intelligence for health: WHO guidance. 2021.

Metcalf J, Moss E. Owning ethics: corporate logics, silicon valley, and the institutionalization of ethics. Soc Res Int Q. 2019;86(2):449–76.

Data Protection Act - OFFICE OF THE DATA PROTECTION COMMISSIONER KENYA [Internet]. 2021 [cited 2023 Sep 30]. https://www.odpc.go.ke/dpa-act/ .

Sharon T, Lucivero F. Introduction to the special theme: the expansion of the health data ecosystem–rethinking data ethics and governance. Big Data & Society. Volume 6. London, England: SAGE Publications Sage UK; 2019. p. 2053951719852969.

Reisman D, Schultz J, Crawford K, Whittaker M. Algorithmic impact assessments: a practical Framework for Public Agency. AI Now. 2018.

Morgan RK. Environmental impact assessment: the state of the art. Impact Assess Proj Apprais. 2012;30(1):5–14.

Samuel G, Richie C. Reimagining research ethics to include environmental sustainability: a principled approach, including a case study of data-driven health research. J Med Ethics. 2023;49(6):428–33.

Kwete X, Tang K, Chen L, Ren R, Chen Q, Wu Z, et al. Decolonizing global health: what should be the target of this movement and where does it lead us? Glob Health Res Policy. 2022;7(1):3.

Abimbola S, Asthana S, Montenegro C, Guinto RR, Jumbam DT, Louskieter L, et al. Addressing power asymmetries in global health: imperatives in the wake of the COVID-19 pandemic. PLoS Med. 2021;18(4):e1003604.

Benatar S. Politics, power, poverty and global health: systems and frames. Int J Health Policy Manag. 2016;5(10):599.

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Acknowledgements

We would like to acknowledge the outstanding contributions of the attendees of GFBR 2022 in Cape Town, South Africa. This paper is authored by members of the GFBR 2022 Planning Committee. We would like to acknowledge additional members Tamra Lysaght, National University of Singapore, and Niresh Bhagwandin, South African Medical Research Council, for their input during the planning stages and as reviewers of the applications to attend the Forum.

This work was supported by Wellcome [222525/Z/21/Z], the US National Institutes of Health, the UK Medical Research Council (part of UK Research and Innovation), and the South African Medical Research Council through funding to the Global Forum on Bioethics in Research.

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JS led the writing, contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. CA contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. PYC contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AE contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. JWG contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. AH contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DJ contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. KL contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. DP contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper. EV contributed to conceptualization and analysis, critically reviewed and provided feedback on drafts of this paper, and provided final approval of the paper.

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Shaw, J., Ali, J., Atuire, C.A. et al. Research ethics and artificial intelligence for global health: perspectives from the global forum on bioethics in research. BMC Med Ethics 25 , 46 (2024). https://doi.org/10.1186/s12910-024-01044-w

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